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不等厚板搭接焊缝缺陷数字X射线检测

迟大钊, 马子奇, 程怡, 王梓明

迟大钊, 马子奇, 程怡, 王梓明. 不等厚板搭接焊缝缺陷数字X射线检测[J]. 焊接学报, 2019, 40(11): 45-48. DOI: 10.12073/j.hjxb.2019400286
引用本文: 迟大钊, 马子奇, 程怡, 王梓明. 不等厚板搭接焊缝缺陷数字X射线检测[J]. 焊接学报, 2019, 40(11): 45-48. DOI: 10.12073/j.hjxb.2019400286
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CHI Dazhao, MA Ziqi, CHENG Yi, WANG Ziming. X-ray based defect testing method for a lap joint with unequal thickness steel plates[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 45-48. DOI: 10.12073/j.hjxb.2019400286
Citation: CHI Dazhao, MA Ziqi, CHENG Yi, WANG Ziming. X-ray based defect testing method for a lap joint with unequal thickness steel plates[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 45-48. DOI: 10.12073/j.hjxb.2019400286
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不等厚板搭接焊缝缺陷数字X射线检测

  • 1.

    哈尔滨工业大学 先进焊接与连接国家重点实验室, 哈尔滨 150001

  • 2.

    上海航天设备制造总厂有限公司, 上海 200245

基金项目: 国家自然科学基金（51375002）；上海航天科技创新基金（SAST2017-063）

X-ray based defect testing method for a lap joint with unequal thickness steel plates

  • 1.

    State Key Laboratory of Advanced Welding and Joining,Harbin Institute of Technology, Habin 150001, China

  • 2.

    Shanghai Aerospace Equipments Manufacturer Co., Ltd., Shanghai 200245, China

摘要

    摘要
  • 摘要: 在不等厚钢板搭接焊缝X射线检测图像中，由于工件及焊缝厚度变化带来的图像背景灰度差异、焊缝区灰度连续变化等，给基于图像处理的缺陷自动识别带来困难.同时，被检测焊缝装卡的空间位置具有不确定性，垂直布置的焊缝和重力方向存在一定角度，不便于缺陷自动识别及定位.文中给出一种基于不变矩的X射线图像校正方法，解决焊缝图像倾斜问题；在此基础上，给出一种图像噪声抑制、背景去除、图像分割及数学形态学相结合的缺陷识别方法.结果表明，所提方法能有效识别不等厚搭接焊缝中的气孔类缺陷，适用于自动检测.
    Abstract: It is difficult to automatically recognize defects using digital image processing method in X-ray image that tested from lap joint with unequal thickness plates. This attributes to the very different gray levels in background image caused by the unequal thickness of the workpiece, and the continuous change gray levels caused by lap joint. Besides, the position of the lap joint is uncertain placed before tested, i.e. there is always an angel between the directions of lap joint and gravity. And this makes it difficult for defect detection and localization. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem of image skew. In addition, a defect detection method combined of image noise suppression, background removal, image segmentation and mathematical morphology is presented. The results show that the proposed method can effectively recognize the gas pores in lap joint with unequal thickness, and it is suitable for automatic detection.
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    • 参考文献(12)
  • [1] 邵家鑫, 都东, 朱新杰, 等. 基于X射线数字化图像处理的双面焊焊缝缺陷检测[J]. 焊接学报, 2010, 31(11):21-24 Shao Jiaxin, Du Dong, Zhu Xinjie, et al. Weld defect detection of double sides weld based on X-ray digitized image[J]. Transactions of the China Welding Institution, 2010, 31(11):21-24
    [2] 迟大钊, 李孙珏, 孙昌立, 等. 基于双目视觉的缺陷深度测量方法[J]. 焊接学报, 2016, 37(11):7-10 Chi Dazhao, Li Sunjue, Sun Changli, et al. Binocular visual based defect buried depth testing method[J]. Transactions of the China Welding Institution, 2016, 37(11):7-10
    [3] 敖波, 王乃波, 何深远, 等. 小管路焊缝内部缺陷的三维X射线成像[J]. 焊接学报, 2016, 37(5):11-14 Ao Bo, Wang Naibo, He Shenyuan, et al. Three dimensional imaging of internal defects in small diameter pipe welding seam by Xray microtomography[J]. Transactions of the China Welding Institution, 2016, 37(5):11-14
    [4] 陈明, 马跃洲, 陈光. X射线线阵实时成像焊缝缺陷检测方法[J]. 焊接学报, 2007, 28(6):81-84 Chen Ming, Ma Yuezhou, Chen Guang. Weld defects detection for X-ray linear array real-time imaging[J]. Transactions of the China Welding Institution, 2007, 28(6):81-84
    [5] Muthannaa B G N, Amaraa M, Meliania M H, et al. Inspection of internal erosion-corrosion of elbow pipe in the desalination station[J]. Engineering Failure Analysis, 2019, 102:293-302.
    [6] Qi J Y, Li J Y. Feature extraction of welding defect based on machine vision[J]. China Welding, 2019, 28(1):56-62.
    [7] Shao J X, Du D, Shi H, et al. A fast and adaptive method for automatic defect detection in various real-time X ray imaging systems[J]. China Welding, 2012, 21(1):8-12.
    [8] Effat Y, Amir M. Contrast enhancement of industrial radiography images by gabor filtering with automatic noise thresholding[J]. Russian Journal of Nondestructive Testing, 2019, 55(1):73-79.
    [9] Hou W H, Wei Y, Jin Yi, et al. Deep features based on a DCNN model for classifying imbalanced weld flaw types[J]. Measurement, 2019, 131:482-489.
    [10] Yenumulaa L, Acharyaa R V, Lingade B M, et al. Radiographic evaluation of gas tungsten arc welded joints used in nuclear applications by X-and gamma-rays[J]. NDT and E International, 2019, 102:144-152.
    [11] Ramou N. Segmentation of weld defects using multiphase level set by the piecewise-smooth mumford-shah model[J]. Russian Journal of Nondestructive Testing, 2019, 55(2):155-161.
    [12] 佟彤, 蔡艳, 孙大为, 等. 基于有监督过渡区的焊缝X射线图像分割[J]. 焊接学报, 2014, 35(3):101-104 Tong Tong, Cai Yan, Sun Dawei, et al. Segmentation of weld seam in X-ray images based on supervised transition region extraction[J]. Transactions of the China Welding Institution, 2014, 35(3):101-104
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  • 收稿日期:  2019-07-05

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